Institutional Underpinnings and Democracy Backsliding in the Perspective of COVID-19

25 05 2021

Helicopter view

project based on the article “Institutional Underpinnings and Democracy Backsliding in the Perspective of COVID-19” and the poster “A study of Democracy Backsliding in the Perspective of COVID-19 using econometrics and machine learning approaches” by PhD Jacek Lewkowicz, Michał Woźniak and Michał Wrzesiński
goal of the article: investigating the drivers of the impact of the political decisions, as a response to the COVID-19 pandemic
materials: use of a novel global dataset covering the period of the first wave of the pandemic (March-June 2020)
methods: various econometric and machine learning tools
conclusions: rule of law and high level of the initial state of democracy prevent from democracy backsliding during the pandemic

Goals of the reproducible project

reproduction of the initial results
improvement of the study by performing robustness checks
structuring the project
clean and readable code for reproducibility
combination of the research methodology developed as a part of a scientific article and a poster from conference MLinPL

Challenges

updated dependent variables:
- new methodology of calculating indexes
- wider time horizon
lack of reproducibility of the code (files sent by e-mails)
econometric part of the study (diagnostic tests) performed initially in STATA
mix of R and Python:
- econometric part in R
- creating databases and performing ML part in Python

Research purpose

COVID-19 and its economic and legal consequences are undoubtedly a very important problem that we have to face.
Governments are using the global pandemic to pursue their opportunistic goals, corruption and power strengthening.
- Therefore, pandemic is concerned as a serious threat to democracy, as governments may try to limit democratic rules under the cover of pandemic management.
- On the other hand, well-managed lockdowns and other means of government interventionism may lead to increased satisfaction with democracy or trust in government.
An alleged democracy recession can be observed in recent years and this backsliding is deeply rooted in institutional environment.
The phenomenon of democracy backsliding remains unexplained despite many attempts.

Research question

What is the relevance of the law and the current state of democracy for susceptibility to democracy backsliding in the face of the COVID-19?

Materials

Group of variables	Variables	Time range
Pandemic data	Pandemic Democratic Violations Index (pandem), Pandemic Democratic Violations Disinformation Index (pandem_dis), Pandemic Backsliding Index (panback)	03.2020 - 07.2020
Democratization data	Rule of law index (rule), Electoral democracy index (polyarchy), Education level index (education), Politico-geographic regions (region_geo)	2019
Economic & Demographic data	Gini Index (gini), GDP pc (gdp_pc), Trade GDP (trade_gdp), Inflation rate (inflation), Oil sale (oil), Minerals sale (mineral),Population Density (density), Income group (income_group)	mostly 2019
Fractionalization data	Ethnic (ethnic_frac), Linguistic (ling_frac) and Religious fractionalization (relig_frac)	2002

Box plots

Adjustments

adding new variable (ratio of covid cases to the population)
adding squared terms for panback regression
robustness checks:
- time periods (Q2, Q3 and Q4 of 2020)
- collinearity (excluding variables that cause too much collinearity)
- missing values (excluding variables with too much imputations)

OLS regression

specification with categorical variables
specification without categorical variables
robustness checks

Initial results - OLS with categorical variables


	OLS models:

	(1) (all variables)			(2) (all variables, excluding polyarchy)			(3) (all variables, excluding rule)
	panback	pandem	pandem_dis	panback	pandem	pandem_dis	panback	pandem	pandem_dis

rule	0.067	0.033	0.027	0.119^**	-0.105^*	-0.097^*	-	-	-

polyarchy	0.091	-0.239^**	-0.216^**	-	-	-	0.145^**	-0.212^***	-0.194^***

education	-0.008	-0.0003	-0.003	-0.006	-0.005	-0.006	-0.008	-0.001	-0.003

ethnic_frac	0.057	-0.0003	0.013	0.046	0.029	0.040	0.066	0.004	0.017

ling_frac	0.017	0.024	-0.006	0.026	0.0003	-0.027	0.015	0.022	-0.007

relig_frac	0.022	-0.015	-0.006	0.019	-0.007	0.002	0.024	-0.013	-0.005

gdp_pc_log	0.010	0.019	0.019	0.004	0.037	0.035	0.017	0.022	0.022

gini_log	0.047	0.075	0.075	0.041	0.090	0.089	0.046	0.074	0.075

inflation	-0.0002	0.00000	-0.0001	-0.0001	-0.0001	-0.0002	-0.0002	-0.00003	-0.0002

trade_gdp_log	-0.045^*	-0.068^**	-0.068^***	-0.043^*	-0.072^**	-0.071^***	-0.044^*	-0.067^**	-0.067^***

oil	0.002	0.001	0.001	0.002	0.001	0.001	0.002	0.001	0.001

mineral	0.004	0.006^**	0.006^**	0.004	0.006^*	0.006^*	0.003	0.006^**	0.006^**

density_log	0.013	0.010	0.010	0.014	0.009	0.009	0.013	0.010	0.010

region_geo FE	yes	yes	yes	yes	yes	yes	yes	yes	yes

income_group FE	yes	yes	yes	yes	yes	yes	yes	yes	yes

Constant	-0.136	0.068	0.047	-0.066	-0.114	-0.118	-0.170	0.051	0.033

Observations	146	146	146	146	146	146	146	146	146
Adjusted R²	0.218	0.318	0.313	0.216	0.283	0.281	0.218	0.323	0.318

Note:	^p<0.1; ^p<0.05; ^**p<0.01

Initial results - OLS without categorical variables


	OLS models:

	(1) (all variables)			(2) (all variables, excluding polyarchy)			(3) (all variables, excluding rule)
	panback	pandem	pandem_dis	panback	pandem	pandem_dis	panback	pandem	pandem_dis

rule	0.019	0.020	-0.008	0.053	-0.148^***	-0.152^***	-	-	-

polyarchy	0.055	-0.272^***	-0.233^***	-	-	-	0.069	-0.257^***	-0.239^***

education	-0.003	0.001	0.0005	-0.003	0.002	0.001	-0.003	0.001	0.001

ethnic_frac	0.029	-0.013	0.009	0.028	-0.008	0.014	0.030	-0.012	0.009

ling_frac	0.034	0.035	0.008	0.032	0.044	0.015	0.035	0.036	0.007

relig_frac	-0.029	-0.044	-0.030	-0.026	-0.060	-0.044	-0.029	-0.044	-0.030

gdp_pc_log	-0.014	-0.002	-0.001	-0.014	-0.005	-0.003	-0.013	-0.0004	-0.001

gini_log	0.056	0.099^*	0.094^*	0.054	0.109^*	0.102^*	0.056	0.099^*	0.094^*

inflation	-0.0003	-0.0001	-0.0003	-0.0003	-0.0003	-0.0004	-0.0003	-0.0001	-0.0003

trade_gdp_log	-0.019	-0.039^*	-0.039^*	-0.019	-0.037	-0.038^*	-0.019	-0.039^*	-0.039^*

oil	0.001	-0.0001	-0.0002	0.0002	0.001	0.001	0.0005	-0.0002	-0.0002

mineral	0.001	0.003	0.003	0.001	0.003	0.002	0.001	0.003	0.003

density_log	0.015^*	0.018^**	0.014^*	0.014^*	0.023^**	0.018^**	0.015^*	0.018^**	0.013^*

region_geo FE	no	no	no	no	no	no	no	no	no

income_group FE	no	no	no	no	no	no	no	no	no

Constant	0.019	0.055	0.065	0.037	-0.035	-0.012	0.008	0.043	0.070


Observations	146	146	146	146	146	146	146	146	146
Adjusted R²	0.057	0.276	0.276	0.059	0.202	0.215	0.063	0.281	0.282

Note:	^p<0.1; ^p<0.05; ^**p<0.01

Reproduced results - OLS with categorical variables


	OLS models:

	(1) (all variables)		(2) (all variables, excluding polyarchy)		(3) (all variables, excluding rule)
	panback	pandem	panback	pandem	panback	pandem

rule	0.539^***	-0.021	0.734^***	-0.149^**	-	-

rule2	-0.506^***	-	-0.676^***	-	-	-

polyarchy	0.702^***	-0.216^**	-	-	0.977^***	-0.233^***

polyarchy2	-0.696^***	-	-	-	-0.965^***	-

cases_ratio	0.00000	-0.00000	0.00000	-0.00000	-0.00000	-0.00000

education_log	-0.020	-0.026	-0.026	-0.047	-0.034	-0.024

ethnic_frac	-0.051	-0.001	-0.059	0.032	0.009	-0.004

ling_frac	0.027	0.019	0.044	-0.005	0.029	0.021

relig_frac	-0.006	-0.030	0.008	-0.021	0.001	-0.031

gdp_pc_log	0.024	0.015	0.016	0.031	0.028	0.013

gini_log	0.067	0.141	0.060	0.165^*	0.091	0.140

inflation	0.0005^*	0.001^*	0.001^**	0.001	0.0001	0.001^*

trade_gdp_log	0.014	-0.003	0.026	-0.005	0.005	-0.003

oil	-0.001	-0.004^*	-0.001	-0.003	-0.001	-0.004^*

mineral	-0.0003	0.002	-0.0001	0.002	-0.0004	0.002

density_log	0.005	0.008	0.011	0.007	0.003	0.008

region_geo FE	yes	yes	yes	yes	yes	yes

income_group FE	yes	yes	yes	yes	yes	yes

Constant	-0.650^*	-0.366	-0.563	-0.608	-0.693^*	-0.349

Observations	143	143	143	143	143	143
Adjusted R²	0.335	0.275	0.286	0.252	0.281	0.281

Note:	^p<0.1; ^p<0.05; ^**p<0.01

Reproduced results - OLS without categorical variables


	OLS models:

	(1) (all variables)		(2) (all variables, excluding polyarchy)		(3) (all variables, excluding rule)
	panback	pandem	panback	pandem	panback	pandem

rule	0.475^***	-0.064	0.734^***	-0.199^***	-	-

rule2	-0.459^***	-	-0.708^***	-	-	-

polyarchy	0.721^***	-0.221^***	-	-	1.081^***	-0.268^***

polyarchy2	-0.726^***	-	-	-	-1.121^***	-

cases_ratio	0.00000	0.00000	0.00000	0.00000	-0.00000	0.00000

education_log	-0.009	0.001	-0.016	-0.0003	-0.004	0.006

ethnic_frac	-0.041	0.013	-0.043	0.023	-0.007	0.010

ling_frac	0.051	0.042	0.060	0.046	0.044	0.039

relig_frac	-0.054^*	-0.099^*	-0.035	-0.110^**	-0.073^*	-0.100^*

gdp_pc_log	0.010	0.0005	0.002	0.0005	0.012	-0.005

gini_log	0.079^**	0.113^*	0.086^*	0.116^*	0.082^*	0.113^*

inflation	0.0004^*	0.0005	0.0005	0.0003	0.00003	0.001

trade_gdp_log	-0.004	-0.004	0.007	-0.003	-0.011	-0.005

oil	-0.001^**	-0.003	-0.002	-0.001	-0.001	-0.003

mineral	-0.001	-0.001	-0.002	-0.001	-0.001	-0.001

density_log	0.010	0.007	0.011	0.010	0.010	0.006

region_geo FE	no	no	no	no	no	no

income_group FE	no	no	no	no	no	no

Constant	-0.432^***	-0.016	-0.355^*	-0.084	-0.422^**	0.019

Observations	143	143	143	143	143	143
Adjusted R²	0.336	0.252	0.278	0.211	0.285	0.253

Note:	^p<0.1; ^p<0.05; ^**p<0.01

Robustness check - collinearity


	OLS models:

	(1)		(2)		(3)
	panback	pandem	panback	pandem	panback	pandem

rule	0.538^***	-0.042	0.769^***	-0.175^***	-	-

rule2	-0.510^***	-	-0.724^***	-	-	-

polyarchy	0.728^***	-0.220^**	-	-	1.050^***	-0.256^***

polyarchy2	-0.723^***	-	-	-	-1.059^***	-




Control variables:	cases_ratio, education_log, fractionalization, gdp_pc_log, inflation, trade_gdp_log, oil, mineral, density_log, region_geo


Note:	^p<0.1; ^p<0.05; ^**p<0.01

Robustness check - collinearity


	OLS models:

	(1)		(2)		(3)
	panback	pandem	panback	pandem	panback	pandem

rule	0.491^***	-0.060	0.744^***	-0.206^***	-	-

rule2	-0.474^***	-	-0.719^***	-	-	-

polyarchy	0.726^***	-0.237^***	-	-	1.108^***	-0.281^***

polyarchy2	-0.737^***	-	-	-	-1.158^***	-




Control variables:	cases_ratio, education_log, fractionalization, gdp_pc_log, inflation, trade_gdp_log, oil, mineral, density_log


Note:	^p<0.1; ^p<0.05; ^**p<0.01

Robustness check - missings


	OLS models:

	(1)		(2)		(3)
	panback	pandem	panback	pandem	panback	pandem

rule	0.575^***	-0.006	0.773^***	-0.158^**	-	-

rule2	-0.535^***	-	-0.719^***	-	-	-

polyarchy	0.702^***	-0.254^***	-	-	1.011^***	-0.259^***

polyarchy2	-0.717^***	-	-	-	-1.025^***	-




Control variables:	cases_ratio, ethnic_frac, ling_frac, relig_frac, gdp_pc_log, inflation, trade_gdp_log, oil, mineral, density_log, region_geo, income_categories


Note:	^p<0.1; ^p<0.05; ^**p<0.01

Robustness check - missings


	OLS models:

	(1)		(2)		(3)
	panback	pandem	panback	pandem	panback	pandem

rule	0.478^***	-0.061	0.751^***	-0.198^***	-	-

rule2	-0.457^***	-	-0.719^***	-	-	-

polyarchy	0.752^***	-0.224^***	-	-	1.110^***	-0.270^***

polyarchy2	-0.762^***	-	-	-	-1.151^***	-




Control variables:	cases_ratio, ethnic_frac, ling_frac, relig_frac, gdp_pc_log, inflation, trade_gdp_log, oil, mineral, density_log


Note:	^p<0.1; ^p<0.05; ^**p<0.01

Random Forest

SHAP summary plots
Permutation importance
Partial Dependence Plots (PDPs) (1 dimensional and 2 dimensional)
Accumulated Local Effects (ALE) plots (1 dimensional and 2 dimensional)

SHAP summary plots - panback

SHAP summary plots - pandem

Permutation Importance - panback

Permutation Importance - pandem

1D PDP - panback vs polyarchy

1D PDP - pandem vs polyarchy

2D PDP - panback vs polyarchy and rule

2D PDP - pandem vs polyarchy and rule

CatBoost

SHAP summary plots
Permutation importance
Partial Dependence Plots (PDPs) (1 dimensional and 2 dimensional)
Accumulated Local Effects (ALE) plots (1 dimensional and 2 dimensional)

SHAP summary plots - panback

SHAP summary plots - pandem

Permutation Importance - panback

Permutation Importance - pandem

1D PDP - panback vs polyarchy

1D PDP - pandem vs polyarchy

2D PDP - panback vs polyarchy and rule

2D PDP - pandem vs polyarchy and rule

Conclusions

We recreated the original results and updated them with the new data and the new methodology.
We confirmed that the results are not distorted by several robustness checks.
Results compared to the original study are analogous.
Influence of law and state of democracy is exceptionally strong. The stronger the rule of law and the higher the level of democracy, the lower is the risk of democracy backsliding in the face of global pandemic.
Our study provides other variables that are worth further investigation such as: GDP per capita, population density, trade as a share of GDP, economic inequality, mineral rents as a share of GDP and linguistic fractionalization.

Helicopter view

Goals of the reproducible project

Challenges

Research purpose

Research question

Materials

Box plots

Adjustments

OLS regression

Initial results - OLS with categorical variables

Initial results - OLS without categorical variables

Reproduced results - OLS with categorical variables

Reproduced results - OLS without categorical variables

Robustness check - collinearity

Robustness check - collinearity

Robustness check - missings

Robustness check - missings

Random Forest

SHAP summary plots - panback

SHAP summary plots - pandem

Permutation Importance - panback

Permutation Importance - pandem

1D PDP - panback vs polyarchy

1D PDP - pandem vs polyarchy

2D PDP - panback vs polyarchy and rule

2D PDP - pandem vs polyarchy and rule

CatBoost

SHAP summary plots - panback

SHAP summary plots - pandem

Permutation Importance - panback

Permutation Importance - pandem

1D PDP - panback vs polyarchy

1D PDP - pandem vs polyarchy

2D PDP - panback vs polyarchy and rule

2D PDP - pandem vs polyarchy and rule

Conclusions

Backup - RF

1D ALE - panback vs polyarchy

1D ALE - pandem vs polyarchy

2D ALE - panback vs polyarchy and rule

2D ALE - pandem vs polyarchy and rule

Backup - CatBoost

1D ALE - panback vs polyarchy

1D ALE - pandem vs polyarchy

2D ALE - panback vs polyarchy and rule

2D ALE - pandem vs polyarchy and rule